Table of Contents

The table of contents is organized into three parts:

  • Part A focuses on core methods that all actuaries studying Loss Data Analytics should know well.

  • Part B describes actuarial practice, links the Part A core methods to these practice areas, and provides additional methodological development to support actuarial practice.

  • Part C describes additional topics that, although certainly falling within the purview of Loss Data Analytics, are of interest to specialized audiences. Some chapters in this part describe practice areas that are limited geographically (e.g., bonus-malus experience rating systems) whereas others are more advanced technically (e.g., dependence modeling).

Part A. Foundations

  • 1. Introduction to loss data analytics (EW (Jed) Frees - Univ of Wisconsin)

  • 2. Modeling loss frequency (Shyamalkumar Nariankadu - Univ of Iowa, Krupa Viswanathan - Temple Univ)

  • 3. Modeling loss severity (Zeinab Amin - American Univ in Cairo)

  • 4. Model selection and comparison (Lisa Gao and EW (Jed) Frees - Univ of Wisconsin)

  • 5. Aggregate loss models (Peng Shi - Univ of Wisconsin)

  • 6. Simulation (Carolina Castro - Univ of Buenos Aires)

Part B. Short term insurance

  • 7. Premium calculation fundamentals (José Garrido, Concordia)

  • 8. Risk classification (Joseph Kim, Yonsei Univ)

    • Technical Supplement. Likelihood and generalized linear models

  • 9. Experience rating using credibility theory (Gary Dean - Ball State Univ)

  • 10. Portfolio management, including reinsurance (Jianxi Su - Purdue Univ and EW (Jed) Frees - Univ of Wisconsin)

  • 11. Loss reserving (Katrien Antonio, Jan Beirlant, Tim Verdonck, KU Leuven)

Part C. Advanced topics

  • 12. Retention and experience rating using bonus-malus (Noriszura Ismail, Univ Kebangsaan Malaysia)

  • 13. Data and systems (Guojun Gan - Univ of Connecticut)

  • 14. Dependence modeling (Nii-Armah Okine and EW (Jed) Frees - Univ of Wisconsin, and Emine Selin Sarıdaş, Mimar Sinan Univ)

  • 15. Health analytics (Margie Rosenberg - Univ of Wisconsin)

  • 16. Topics in statistical inference, including GLM details

Appendices

  • Appendix A. Review of statistical inference

  • Appendix B. Iterated expectations

  • Appendix C. Maximum likelihood theory

    • (Lei (Larry) Hua - Northern Illinois Univ and EW (Jed) Frees - Univ of Wisconsin)

Discussion

  • To get a better sense of what we mean by the chapter titles, you will find section headings under our Detailed Table of Contents page.

    • Visit our Content Mapping page to see how the table of contents relates to various professional actuarial organizations and competing texts.

    • Author commitments are in parens ()